风是历届冬奥会十分关注的气象条件之一，是影响北京冬奥会山地赛事的首要气象因素，复杂地形下局地风场的精细化时空分布规律可以为赛道施工、风场预报、防风措施提供重要的理论依据。本文利用2017年12月至2022年3月北京冬奥会延庆高山区不同海拔常规地面自动气象站和冬奥赛道加密站逐小时观测资料，研究了冬季（12月至次年2月）和早春（3月，残奥会期间）复杂地形下局地风场的精细化时空分布特征，重点对比分析了风速风向频率分布、日变化规律及季节变化差异。首先采用K-Means聚类方法将所有自动站分为四组，组 1到组 4分别代表延庆低海拔远郊区、东北山麓过渡区、西南过渡区、高海拔山顶区，然后以组为单位进行精细化特征分析。结果表明：（1）大风发生频率与海拔紧密相关，海拔越高，则大风发生频率越高。组 1 和组 2（海拔 1000 m 以下）小风（<3. 3 m·s^-1）发生频率超过80%，而大风（≥10. 7 m·s^-1）占比为0%；组3（海拔1000 m以上）小风发生频率下降至75%以下，并偶尔发生大风，但占比不足 1%；组 4（海拔 1800 m 以上）风速频率分布发生质变，大风出现频率增加至 10% 以上，其中冬季远高于早春。（2）风向分布特征存在显著的局地性差异。组4受大尺度冬季风环流主导，盛行偏西北风，极少出现其他风向；组 1~3受大尺度环流、山谷风环流和局地下垫面综合作用，各个风向以不同频率发生。（3）日循环变化呈现高、低海拔截然相反的特征。组 1~3风速夜间小、白天大，组 4则表现为夜间大、白天小、午间小风“窗口期”规律；组 1~3存在明显的风向日夜转换，转换时间为日出和日落后，组4风向不存在日变化。（4）从季节变化看，早春风场和冬季存在较明显的差异，且局地差异较大。相比冬季，早春组1~2白天风速增大，组3夜间风速降低，组4全天风速均大幅减小；早春风向相对更多变，组 1东北风向显著增多，组 2谷风转山风时间比冬季推迟 3 h，组3~4西南风向所有增加。上述研究结果有助于深入认识复杂地形下近地面局地风场的精细化时空规律，可为冬奥会及小尺度山地气象监测和预报提供重要的背景参考。

Wind is one of the most important meteorological conditions in previous Winter Olympics，and it is the primary factor that affects the mountain events for Beijing Winter Olympics. Understanding the fine distribution law of wind can provide important theoretical basis for track construction，wind forecast and prevention measures. Using hourly observation data from surface automatic weather stations at different altitudes in Yanqing mountain area of Beijing Winter Olympics from December 2017 to March 2022，this study investigated the characteristics of local wind field during winter and early spring（Mar，Paralympics period）under complex terrain， focusing on comparing the frequency of wind speeds and directions，as well as the diurnal and seasonal variations. Firstly，all stations were grouped into four categories using the K-Means clustering algorithm，and Groups 1 to 4 represent the low-elevation Yangqing suburb area，the northeastern foothills transition area，the southwest‐ ern transition area and the high-elevation mountain top area，respectively. Subsequently，fine-grained characteristic analysis was conducted on each group separately. Results show that：（1）The frequency of strong winds is closely related to the altitude，with higher altitudes generally having a higher frequency of strong winds. In Groups 1~2（altitude below 1000 m），the frequency of light winds（≤3. 3 m·s^-1）exceeds 80%，while the propor-tion of strong winds（≥10. 7 m·s^-1）is 0%. In Group 3（above 1000 m），the frequency of light winds decreases to below 75%，and strong winds occasionally occur for less than 1%. In Group 4（above 1800 m），there is a significant shift in the wind speed frequency distribution，with the frequency of strong winds increasing to above 10%， which is much higher during winter compared to early spring.（2）There are significant local variations in the distribution characteristics of wind directions. Group 4 is primarily dominated by large-scale winter monsoonal circulation，resulting in a prevailing northwesterly wind，with rare concurrence of other wind directions. Groups 1~ 3 are influenced by a combination of large-scale circulation，valley wind circulation and underlying surface conditions，leading to different frequencies for each wind direction.（3）The diurnal variation exhibits contrasting characteristics between high and low elevations. Groups 1~3 show lower wind speeds at night and higher wind speeds during the day，while Group 4 shows a reserved pattern and an obvious small wind “window period” in midday. Groups 1~3 exhibit distinct daily transitions in wind direction，occurring after sunrise and sunset，where‐ as Group 4 does not show any diurnal change.（4）From a seasonal perspective，there are significant local differences between early spring and winter. Compared to winter，Group 2 exhibits a daytime wind speed increase in early spring，and Group 3 exhibits a nighttime decrease，while Group 4 exhibits a significant decrease in wind speeds throughout the day. Wind directions in early spring are relatively more variable，with an evident increase in northeasterly winds in Group 1，a delay of about 3 hours in the transition of valley wind circulation in Group 2，and an increase in southwesterly winds in Groups 3~4. This study contributes to a deeper comprehension of the fine-scale spatiotemporal patterns of near-surface local wind fields within complex terrains，and can offer crucial background clues for Winter Olympics and small-scale mountainous meteorological monitoring and forecasting.